[1] Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. Designing neural network architectures using reinforcement learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulouse, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. URL https://openreview.net/forum?id=S1c2cvqee.
[2] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyperparameter optimization. In Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS'11, page 2546-2554, Red Hook, NY, USA, 2011. Curran Associates Inc. ISBN 9781618395993.
[3] James Bergstra, Daniel Yamins, and David Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Sanjoy Dasgupta and David McAllester, editors, Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, pages 115-123, Atlanta, Georgia, USA, 17-19 Jun 2013. PMLR. URL https://proceedings.mlr.press/v28/bergstra13.html.
[4] Christos Boutsidis, Petros Drineas, and Malik Magdon-Ismail. Near-optimal coresets for least-squares regression. IEEE transactions on information theory, 59(10):6880-6892, 2013.
[5] Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia. Selection via proxy: Efficient data selection for deep learning. arXiv preprint arXiv:1906.11829, 2019.
[6] Abir De, Paramita Koley, Niloy Ganguly, and Manuel Gomez-Rodriguez. Regression under human assistance. AAAI, 2020.
[7] Abir De, Nastaran Okati, Ali Zarezade, and Manuel Gomez-Rodriguez. Classification under human assistance. AAAI, 2021.
[8] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2009. doi: 10.1109/CVPR.2009.5206848.
[9] Xuanyi Dong and Yi Yang. Nas-bench-201: Extending the scope of reproducible neural architecture search, 2020. URL https://arxiv.org/abs/2001.00326.
[10] Dan Feldman. Core-sets: Updated survey. In *Sampling Techniques for Supervised or Unsupervised Tasks*, pages 23–44. Springer, 2020.
[11] Satoru Fujishige. Submodular functions and optimization. Elsevier, 2005.
[12] Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In International conference on machine learning, pages 1263-1272. PMLR, 2017.
[13] Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. 2007.
[14] Chengcheng Guo, Bo Zhao, and Yanbing Bai. Deepcore: A comprehensive library for coreset selection in deep learning. In Database and Expert Systems Applications: 33rd International Conference, DEXA 2022, Vienna, Austria, August 22-24, 2022, Proceedings, Part I, pages 181-195. Springer, 2022.
[15] Sariel Har-Peled and Soham Mazumdar. On coresets for k-means and k-median clustering. In Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, pages 291–300, 2004.
[16] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015. URL http://arxiv.org/abs/1512.03385.